Sequential Analysis in a Bayesian Model of Diastolic Blood Pressure Measurement

Abstract

A sequential method for diagnosing or excluding hypertension based on the Bayesian model of diastolic blood pressure presented in a companion article is presented. The likelihood ratio method of Wald is modified to include the effects of a prior probability distribution and to constrain the strategy to achieve specified positive and negative predictive values. The resulting formulas for upper and lower limits to diagnose and exclude diastolic hypertension can be evaluated using a hand calculator and a table of areas of the standard normal distribution. The strategy is illustrated for a population having a blood pressure distribution similar to that of the cohort screened for participation in the Hypertension Detection and Follow-up Program, with 90 mm Hg as the cutoff defining hypertension and required positive and negative predictive values of 95%. The performance of the strategy was simulated using Monte Carlo methods. The median number of readings required for diagnosis is three, and 80% of subjects are diagnosed in 11 or fewer readings. In contrast to the strategy's 95% predictive values, a fixed-number-of-measurements strategy requiring the same mean num ber of measurements has a positive predictive value of only 83% and a negative predictive value of 96%. When the parameters of the model have been properly measured or estimated, this method is practical, efficient, and accurate for diagnosing hypertension in a known population. Key words: Bayes' theorem; sequential analysis; hypertension diagnosis. (Med Decis Making 8:191-196, 1988)